import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case								=    logging.get_logger(__name__)

_snake_case								=    """▁"""

_snake_case								=    {"""vocab_file""": """prophetnet.tokenizer"""}

_snake_case								=    {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

_snake_case								=    {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

_snake_case								=    {
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}




def 	_UpperCamelCase (      snake_case__				)    -> Any:
							__UpperCAmelCase    :      Optional[int]       = collections.OrderedDict()
							with open(__snake_case,     "r",     encoding="utf-8"				) as reader:
														__UpperCAmelCase    :      Optional[int]       = reader.readlines()
							for index, token in enumerate(__snake_case				):
														__UpperCAmelCase    :      int       = token.rstrip("\n"				)
														__UpperCAmelCase    :      Any       = index
							return vocab







class        _snake_case    (    a_					):
					lowerCamelCase__:		Union[str, Any]	=  VOCAB_FILES_NAMES
					lowerCamelCase__:		List[Any]	=  PRETRAINED_VOCAB_FILES_MAP
					lowerCamelCase__:		str	=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCamelCase__:		Optional[Any]	=  ["input_ids", "attention_mask"]
					def __init__(	self:					Optional[int]						,	__lowerCamelCase:					List[str]						,	__lowerCamelCase:					Union[str, Any]="[SEP]"						,	__lowerCamelCase:					List[Any]="[SEP]"						,	__lowerCamelCase:					Any="[SEP]"						,	__lowerCamelCase:					str="[UNK]"						,	__lowerCamelCase:					int="[PAD]"						,	__lowerCamelCase:					List[Any]="[CLS]"						,	__lowerCamelCase:					Union[str, Any]="[MASK]"						,	__lowerCamelCase:					Optional[Dict[str, Any]] = None						,	**__lowerCamelCase:					Any						,	)     ->					int:
												__UpperCAmelCase    :      List[str]       = {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=UpperCamelCase_						,	eos_token=UpperCamelCase_						,	sep_token=UpperCamelCase_						,	unk_token=UpperCamelCase_						,	pad_token=UpperCamelCase_						,	cls_token=UpperCamelCase_						,	mask_token=UpperCamelCase_						,	sp_model_kwargs=self.sp_model_kwargs						,	**UpperCamelCase_						,	)

												try:
																			import sentencepiece as spm
												except ImportError:
																			logger.warning(
																			    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																			    " pip install sentencepiece"  )
																			raise

												__UpperCAmelCase    :      Dict       = spm.SentencePieceProcessor(**self.sp_model_kwargs  )
												self.sp_model.Load(str(UpperCamelCase_  )  )
												__UpperCAmelCase    :      Optional[int]       = vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												__UpperCAmelCase    :      int       = {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

												for i in range(10  ):
																			__UpperCAmelCase    :      Optional[int]       = f'''[unused{i}]'''
																			__UpperCAmelCase    :      str       = 5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												__UpperCAmelCase    :      int       = 12
												__UpperCAmelCase    :      Tuple       = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																			self.unique_no_split_tokens.append(UpperCamelCase_  )
					def __getstate__(	self:					int  )     ->					List[str]:
												__UpperCAmelCase    :      List[Any]       = self.__dict__.copy()
												__UpperCAmelCase    :      str       = None
												return state
					def __setstate__(	self:					Dict						,	__lowerCamelCase:					Optional[int]  )     ->					Dict:
												__UpperCAmelCase    :      Union[str, Any]       = d
												try:
																			import sentencepiece as spm
												except ImportError:
																			logger.warning(
																			    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																			    " pip install sentencepiece"  )
																			raise

												# for backward compatibility
												if not hasattr(self						,	"sp_model_kwargs"  ):
																			__UpperCAmelCase    :      Union[str, Any]       = {}

												__UpperCAmelCase    :      Optional[Any]       = spm.SentencePieceProcessor(**self.sp_model_kwargs  )
												self.sp_model.Load(self.vocab_file  )
					def  _lowerCamelCase			(	self:					List[Any]						,	__lowerCamelCase:					List[int]						,	__lowerCamelCase:					Optional[List[int]] = None						,	__lowerCamelCase:					bool = False  )     ->					Union[str, Any]:

												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=UpperCamelCase_						,	token_ids_a=UpperCamelCase_						,	already_has_special_tokens=UpperCamelCase_  )

												if token_ids_a is None:
																			return ([0] * len(UpperCamelCase_  )) + [1]
												return ([0] * len(UpperCamelCase_  )) + [1] + ([0] * len(UpperCamelCase_  )) + [1]
					def  _lowerCamelCase			(	self:					List[str]						,	__lowerCamelCase:					List[int]						,	__lowerCamelCase:					Optional[List[int]] = None  )     ->					str:
												__UpperCAmelCase    :      Dict       = [self.sep_token_id]

												if token_ids_a is None:
																			return len(token_ids_a + sep  ) * [0]
												return len(token_ids_a + sep + sep + token_ids_a + sep  ) * [0]
					@property
					def  _lowerCamelCase			(	self:					Dict  )     ->					Any:
												return len(self.sp_model  ) + self.fairseq_offset
					def  _lowerCamelCase			(	self:					Union[str, Any]  )     ->					Union[str, Any]:
												__UpperCAmelCase    :      Tuple       = {self.convert_ids_to_tokens(UpperCamelCase_  ): i for i in range(self.vocab_size  )}
												vocab.update(self.added_tokens_encoder  )
												return vocab
					def  _lowerCamelCase			(	self:					int						,	__lowerCamelCase:					str  )     ->					int:
												return self.sp_model.encode(UpperCamelCase_						,	out_type=UpperCamelCase_  )
					def  _lowerCamelCase			(	self:					str						,	__lowerCamelCase:					Union[str, Any]  )     ->					Union[str, Any]:

												if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
												__UpperCAmelCase    :      Union[str, Any]       = self.sp_model.PieceToId(UpperCamelCase_  )

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
					def  _lowerCamelCase			(	self:					Dict						,	__lowerCamelCase:					int  )     ->					Union[str, Any]:

												if index in self.fairseq_ids_to_tokens:
																			return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset  )
					def  _lowerCamelCase			(	self:					Dict						,	__lowerCamelCase:					Optional[Any]  )     ->					List[str]:
												__UpperCAmelCase    :      str       = "".join(UpperCamelCase_  ).replace(UpperCamelCase_						,	" "  ).strip()
												return out_string
					def  _lowerCamelCase			(	self:					Tuple						,	__lowerCamelCase:					str						,	__lowerCamelCase:					Optional[str] = None  )     ->					List[str]:

												if not os.path.isdir(UpperCamelCase_  ):
																			logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
																			return
												__UpperCAmelCase    :      Optional[Any]       = os.path.join(
												    UpperCamelCase_						,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

												if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase_  ) and os.path.isfile(self.vocab_file  ):
																			copyfile(self.vocab_file						,	UpperCamelCase_  )
												elif not os.path.isfile(self.vocab_file  ):
																			with open(UpperCamelCase_						,	"wb"  ) as fi:
																										__UpperCAmelCase    :      Optional[Any]       = self.sp_model.serialized_model_proto()
																										fi.write(UpperCamelCase_  )

												return (out_vocab_file,)





					def  _lowerCamelCase			(	self:					int						,	__lowerCamelCase:					List[int]						,	__lowerCamelCase:					Optional[List[int]] = None  )     ->					Tuple:

												if token_ids_a is None:
																			return token_ids_a + [self.sep_token_id]
												__UpperCAmelCase    :      List[Any]       = [self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep












"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							A						(				__snake_case: Tuple  )    ->		Optional[Any]:

       """simple docstring"""





       if (
           (cp >= 0x4_E00 and cp <= 0x9_FFF)
           or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
           or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
           or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
           or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
           or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
           or (cp >= 0xF_900 and cp <= 0xF_AFF)
           or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
       ):  #
              return True

       return False


def 							A						(				__snake_case: str  )    ->		Optional[Any]:

       """simple docstring"""





       for char in word:
              __magic_name__      				=    ord(__snake_case  )
              if not _is_chinese_char(__snake_case  ):
                     return 0
       return 1


def 							A						(				__snake_case: List[str]  )    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    set()

       for token in tokens:
              __magic_name__      				=    len(__snake_case  ) > 1 and is_chinese(__snake_case  )
              if chinese_word:
                     word_set.add(__snake_case  )
       __magic_name__      				=    list(__snake_case  )
       return word_list


def 							A						(				__snake_case: List[str] ,     __snake_case: set()  )    ->		str:

       """simple docstring"""





       if not chinese_word_set:
              return bert_tokens
       __magic_name__      				=    max([len(__snake_case  ) for w in chinese_word_set]  )

       __magic_name__      				=    bert_tokens
       __magic_name__				,  __magic_name__      				=    0, len(__snake_case  )
       while start < end:
              __magic_name__      				=    True
              if is_chinese(bert_word[start]  ):
                     __magic_name__      				=    min(end - start ,     __snake_case  )
                     for i in range(__snake_case ,     1 ,     -1  ):
                            __magic_name__      				=    ''.join(bert_word[start : start + i]  )
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,     start + i  ):
                                          __magic_name__      				=    '##' + bert_word[j]
                                   __magic_name__      				=    start + i
                                   __magic_name__      				=    False
                                   break
              if single_word:
                     start += 1
       return bert_word


def 							A						(				__snake_case: List[str] ,     __snake_case: LTP ,     __snake_case: BertTokenizer  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    []

       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,     tasks=['cws']  ).cws
              __magic_name__      				=    [get_chinese_word(__snake_case  ) for r in res]
              ltp_res.extend(__snake_case  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    bert_tokenizer(lines[i : i + 1_0_0] ,     add_special_tokens=__snake_case ,     truncation=__snake_case ,     max_length=5_1_2  )
              bert_res.extend(res['input_ids']  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for input_ids, chinese_word in zip(__snake_case ,     __snake_case  ):
              __magic_name__      				=    []
              for id in input_ids:
                     __magic_name__      				=    bert_tokenizer._convert_id_to_token(__snake_case  )
                     input_tokens.append(__snake_case  )
              __magic_name__      				=    add_sub_symbol(__snake_case ,     __snake_case  )
              __magic_name__      				=    []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__snake_case  ):
                     if token[:2] == "##":
                            __magic_name__      				=    token[2:]
                            # save chinese tokens' pos
                            if len(__snake_case  ) == 1 and _is_chinese_char(ord(__snake_case  )  ):
                                   ref_id.append(__snake_case  )
              ref_ids.append(__snake_case  )

       assert len(__snake_case  ) == len(__snake_case  )

       return ref_ids


def 							A						(				__snake_case: Optional[Any]  )    ->		List[str]:

       """simple docstring"""





       with open(args.file_name ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    f.readlines()
       __magic_name__      				=    [line.strip() for line in data if len(__snake_case  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       __magic_name__      				=    LTP(args.ltp  )  # faster in GPU device
       __magic_name__      				=    BertTokenizer.from_pretrained(args.bert  )

       __magic_name__      				=    prepare_ref(__snake_case ,     __snake_case ,     __snake_case  )

       with open(args.save_path ,     'w' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.dumps(__snake_case  ) + '\n' for ref in ref_ids]
              f.writelines(__snake_case  )


if __name__ == "__main__":
 snake_case : Tuple     		=		argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     required=False,
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""",
     required=False,
     type=str,
     default="""./resources/ltp""",
     help="""resources for LTP tokenizer, usually a path""",
 )
 parser.add_argument(
     """--bert""",
     required=False,
     type=str,
     default="""./resources/robert""",
     help="""resources for Bert tokenizer""",
 )
 parser.add_argument(
     """--save_path""",
     required=False,
     type=str,
     default="""./resources/ref.txt""",
     help="""path to save res""",
 )

 snake_case : Any     		=		parser.parse_args()
 main(args)